Tracking return on investment (ROI) from television (TV) is an unsolved problem for advertising. Some of these attempts are set forth below.
A. IPTV—Many commentators have written that efforts such as internet protocol enabled television (IPTV) will eventually enable TV conversions to be tracked via conversion tracking pixels similar to those in place today throughout the web. IPTVs obtain their TV content from the internet and use hypertext transport protocol (HTTP) for requesting content. However, there are many technical challenges before tracking conversions using IPTVs becomes a reality. Today, only about 8% of US TV households have IP enabled TV. Attempts to introduce IPTVs such as Google® TV and Apple® TV have met with only lukewarm interest. Even if web-like conversion tracking becomes possible using TV, it still won't capture all of the activity such as brand recognition leading to delayed conversions, and purchasing at retail stores.
B. RFI Systems—Some companies have experimented with methods for enabling existing TVs to be able to support a direct “purchase” from “the consumer's lounge room” using present-day Set-top box systems and remote controls. Some system providers have developed an on-screen “bug” that appears at the bottom of the screen, and asks the consumer if they would want more information or a coupon. The consumer can click on their remote control to accept. Although promising, adoption of remote control RFI systems is constrained by lack of hardware support and standards. These systems also have the same disadvantages of IPTV, in being unable to track delayed conversions.
C. Panels—One of the most common fallbacks in the TV arena—when faced with difficult-to-measure effects—is to use volunteer, paid panels to find out what people do after they view advertisements. There are several companies that use panels to try to track TV exposures to sales. One advantage of this method is that it makes real-time tracking possible. However, in all cases, the small size of the panel (e.g., 25,000 people for some panels) presents formidable challenges for extrapolation and difficulty finding enough transactions to reliably measure sales. Another problem with the panel approach is the cost of maintaining the panels.
D. Mix Models—If data from previous campaigns has been collected, then it may be possible to regress the historical marketing channel activity (e.g., impressions bought on TV ads, radio ads, web ads, print, etc.) against future sales. Unfortunately, such an approach offers no help if the relationships change in the future. Moreover, such an approach does not provide real time tracking. In addition, historical factors are rarely orthogonal—for example, retailers often execute coordinated advertising across multiple channels correlated in time on purpose in order to exploit seasonal events. This can lead to a historical factors matrix that aliases interactions and even main effects. Even if there are observations in which all main effects vary orthogonally, in practice there may be too few cases for estimation.
E. Market Tests—Market Tests overcome the problems of mix models by creating orthogonal experimental designs to study the phenomena under question. TV is run in some geographic areas and not others, and sales then compared between the two. Market tests rely on local areas to compare treatments to controls. One problem typical to market tests is their inability to be used during a national campaign. Once a national television ad campaign is under way, there are no longer any controls that aren't receiving the TV signal of the ad campaign. This causes additional problems—for example, a market test might be executed flawlessly in April, and then a national campaign starts up in May. However, some external event is now in play during May, and the findings compiled meticulously during April are no longer valid. This is a problem of the market test being a “research study” that becomes “stale” as soon as the national campaign is started. Thus, market tests also fail to provide real time tracking.
None of the above methods or techniques provide practical methods to effectively track the effects of TV advertising in real-time in a national campaign. The lack of conversion tracking on TV has arguably led to a proliferation of untargeted, irrelevant ads. Solving the TV conversion tracking problem could be of great significance for computational advertising.